
package jsat.distributions;

import jsat.linear.Vec;
import static java.lang.Math.*;
import static jsat.math.SpecialMath.*;
import jsat.text.GreekLetters;

/**
 *
 * @author Edward Raff
 */
public class LogNormal extends Distribution
{
    double mu;
    double sig;

    public LogNormal()
    {
        this(0, 1);
    }
    
    
    public LogNormal(double mu, double sig)
    {
        this.mu = mu;
        this.sig = sig;
    }

    
    
    @Override
    public double pdf(double x)
    {
        if(x <= 0)
            return 0;
        double num = exp(-pow(log(x)-mu, 2)/(2*sig*sig));
        double denom = x*sqrt(2*PI*sig*sig);
        return num/denom;
    }

    @Override
    public double cdf(double x)
    {
        if(x <= 0)
            return 0;
        return 0.5 + 0.5*erf( (log(x)-mu)/sqrt(2*sig*sig) );
    }

    @Override
    public double invCdf(double p)
    {
        double expo = mu+sqrt(2)*sqrt(sig*sig)*invErf(2*p-1.0);
        return exp(expo);
    }

    @Override
    public double min()
    {
        return 0;
    }

    @Override
    public double max()
    {
        return Double.POSITIVE_INFINITY;
    }

    @Override
    public String getDistributionName()
    {
        return "LogNormal";
    }

    @Override
    public String[] getVariables()
    {
        return new String[]{GreekLetters.mu , GreekLetters.sigma};
    }

    @Override
    public double[] getCurrentVariableValues()
    {
        return new double[]{mu, sig};
    }

    @Override
    public void setVariable(String var, double value)
    {
        if(var.equals(GreekLetters.mu))
            mu = value;
        else if(var.equals(GreekLetters.sigma))
            if(value <= 0)
                throw new ArithmeticException("Standard deviation must be > 0, not " + value );
            else
                sig = value;
    }

    @Override
    public Distribution clone()
    {
        return new LogNormal(mu, sig);
    }

    @Override
    public void setUsingData(Vec data)
    {
        double mean = data.mean();
        double var = data.variance();
        
        mu = log(mean) - 0.5*log(1 + var/(mean*mean));
        sig = sqrt(1 + var/(mean*mean));
    }

    @Override
    public double mean()
    {
        return exp(mu + sig*sig*0.5);
    }

    @Override
    public double median()
    {
        return exp(mu);
    }

    @Override
    public double mode()
    {
        return exp(mu-sig*sig);
    }

    @Override
    public double variance()
    {
        return expm1(sig*sig)*exp(2*mu+sig*sig);
    }

    @Override
    public double skewness()
    {
        return (exp(sig*sig)+2)*sqrt(expm1(sig*sig));
    }
    
}
